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Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry
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Proteoform characterization based on top-down mass spectrometry.

Jiancheng Zhong1, Yusui Sun1, Minzhu Xie1

  • 1College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan, China.

Briefings in Bioinformatics
|March 3, 2020
PubMed
Summary
This summary is machine-generated.

This review details proteoform identification methods using top-down mass spectrometry (MS). It covers spectral preprocessing, database filtering, and scoring techniques essential for characterizing disease-related protein alterations.

Keywords:
deconvolutionposttranslational modificationproteoform identificationproteoform localizationtop-down mass spectrometry

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Area of Science:

  • Biochemistry and Molecular Biology
  • Analytical Chemistry
  • Proteomics

Background:

  • Proteins are key to biological functions, and their altered forms (proteoforms) are directly linked to disease pathology.
  • Characterizing proteoforms, including primary structure alterations (PSAs), is crucial for medical advancements.
  • Top-down mass spectrometry (MS) enables proteoform characterization but faces significant challenges.

Purpose of the Study:

  • To comprehensively review existing methods for proteoform identification.
  • To provide an overview of spectral preprocessing, database filtering, and scoring techniques for proteoform characterization.
  • To evaluate and compare commonly used methods in the field.

Main Methods:

  • Review of literature on top-down mass spectrometry-based proteoform identification.
  • Summary of deconvolution algorithms for spectral data.
  • Analysis of filtering algorithms and scoring methods for proteoform localization.

Main Results:

  • Identification of proteoform characterization as a critical step in understanding disease.
  • Compilation of various techniques including spectral preprocessing, database filtering, and scoring methods.
  • Evaluation and comparison of current proteoform identification approaches.

Conclusions:

  • Proteoform identification is central to characterizing disease-related protein changes.
  • The review consolidates knowledge on essential techniques for proteoform analysis.
  • This work aims to guide researchers in developing novel and efficient proteoform characterization algorithms.